What Really Happens When You Ask ChatGPT a Question?

2026-06-20 · Source: LLM on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Novice, long

Summary

Large Language Models (LLMs) such as ChatGPT, Gemini, and Claude process user prompts through a detailed sequence, starting with tokenization, where input text is broken into smaller units and mapped to numerical IDs. These IDs are then converted into embeddings, which are vector representations capturing semantic meaning. Positional encoding adds information about word order, crucial for understanding context. The core of this process lies within the Transformer architecture, specifically its multi-head self-attention mechanisms, which enable the model to discern relationships between words and their contextual relevance. These operations occur within multiple Transformer blocks, each refining the understanding. LLMs undergo two phases: training, where they learn to predict the next token by updating internal weights, and inference, where they apply learned patterns to generate responses. The final token prediction involves a linear layer for scoring and a Softmax function for probability assignment, with various decoding strategies like greedy or temperature sampling explaining varied outputs for identical prompts.

Key takeaway

For AI Engineers optimizing LLM performance, understanding the internal tokenization, embedding, and attention mechanisms is crucial. You should consider how different decoding strategies like temperature or top-k sampling impact output diversity and determinism for specific applications. This knowledge allows you to fine-tune model behavior and interpret unexpected responses more effectively, ensuring your deployments meet desired consistency or creativity requirements.

Key insights

LLMs operate as advanced next-token prediction systems, utilizing Transformers for contextual language understanding.

Principles

• Tokenization bridges human language to machine processing.
• Positional encoding is vital for sequence order.
• Self-attention deciphers word relationships and context.

Method

LLMs process input via tokenization, embedding, positional encoding, and multiple Transformer blocks, culminating in linear layer scoring and Softmax for next token prediction.

In practice

• Different tokenizers yield varied token splits.
• Decoding strategies influence response diversity.
• LLM outputs are not always deterministic.

Topics

• Large Language Models
• Transformer Architecture
• Tokenization
• Embeddings
• Self-Attention
• Next Token Prediction
• Decoding Strategies

Best for: AI Student, AI Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by LLM on Medium.